metadata

license: mit
datasets:
  - Yelp/yelp_review_full
language:
  - en
metrics:
  - accuracy
base_model:
  - distilbert/distilbert-base-uncased
pipeline_tag: text-classification
library_name: transformers
tags:
  - sentiment-analysis

Product Review Sentiment Analyzer

This model classifies product reviews as positive, negative, or neutral. It was fine-tuned on the IMDB dataset using DistilBERT as the base model.

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("yourusername/product-review-sentiment-analyzer")
model = AutoModelForSequenceClassification.from_pretrained("arpitk/product-review-sentiment-analyzer")

text = "This product exceeded my expectations!"
inputs = tokenizer(text, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)
    
probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
prediction = torch.argmax(probabilities, dim=-1).item()

labels = ["Negative", "Positive", "Neutral"]  # Adjust based on your model's output order
print(f"Sentiment: {labels[prediction]}")

This project demonstrates how to build and deploy a sentiment analysis model for product reviews using free resources.

Project Overview

Fine-tuned a DistilBERT model on Yelp product reviews
Classifies reviews as Positive, Negative, or Neutral
Achieved XX% accuracy on the evaluation dataset

Live Demo

Try the model yourself: Hugging Face Space

Technologies Used

Google Colab (free GPU)
Hugging Face Transformers
PyTorch
Gradio for the web interface

Project Structure

notebooks/: Contains the Jupyter notebook for model development
app/: Contains the Gradio app for deployment
src/: Contains source code for data processing and model training

How to Use

Clone this repository
Run the notebook to train your own model
Or use the pre-trained model: arpitk/product-review-sentiment-analyzer

Results

The model achieves 90% accuracy on the test set. Here are some example predictions:

"This product exceeded my expectations!" → Positive (0.95)
"It broke after two days of use." → Negative (0.89)
"The product is okay, but a bit overpriced." → Neutral (0.78)